Involving Disadvantaged People in Dialogue: Arguments and Examples from Mental Health Care
Bibliographic record
Abstract
This paper examines the theoretical and practical basis for engaging in dialogue with very disadvantaged people. Using a selective literature review, conceptual analysis, and clinical examples, we explore the reasonable limits of dialogue with disadvantaged populations in order to better understand dialogue, as well as to explore ways to effectively involve disadvantaged people in dialogue. Although people with serious mental illness represent only one very disadvantaged population, we suggest that examining dialogue with this population can serve as a test case for dialogue with disadvantaged people more generally. A recovery-oriented approach can support dialogue processes with people who have mental illness, as their recovery may require, or at least benefit from, dialogue. The inclusion of two clinical scenarios serves to highlight differences in clinical and personal recovery outcomes when dialogue is and is not present in mental health care. Furthermore, although it is not required from a standard principles-based bioethical approach, involving people with mental health issues in dialogue can complement a standard bioethics approach, through dialogical bioethics. A dialogical approach goes beyond the standard principles of bioethics by means of a process that allows relevant bioethical principles to be prioritised, based in part on the person’s informed choice. Overall, our findings suggest that involving very disadvantaged people in dialogue – in this case, people with serious mental illness – is not only possible, it is plausible and can be constructive in relation to a variety of dialogical aims that range from informing to supporting to decision-making processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.030 | 0.034 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".